{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t-aGxVgKyBD2"
   },
   "source": [
    "#从kaggle上面下载数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 186
    },
    "colab_type": "code",
    "id": "nVE2Hkb5AgDb",
    "outputId": "6fbacb41-d556-4b19-eae7-4832ac5b1e57"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mesh-tensorflow          0.1.4      \n",
      "tensorflow               1.15.0     \n",
      "tensorflow-datasets      1.3.0      \n",
      "tensorflow-estimator     1.15.1     \n",
      "tensorflow-gan           2.0.0      \n",
      "tensorflow-hub           0.7.0      \n",
      "tensorflow-metadata      0.15.0     \n",
      "tensorflow-privacy       0.2.2      \n",
      "tensorflow-probability   0.7.0      \n"
     ]
    }
   ],
   "source": [
    "!pip list|grep  tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 831
    },
    "colab_type": "code",
    "id": "Mpvt0IlRCDrR",
    "outputId": "53898375-30cf-4456-c95e-2294ec48b477"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==1.14.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/f0/96fb2e0412ae9692dbf400e5b04432885f677ad6241c088ccc5fe7724d69/tensorflow-1.14.0-cp36-cp36m-manylinux1_x86_64.whl (109.2MB)\n",
      "\u001b[K     |████████████████████████████████| 109.2MB 96kB/s \n",
      "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.8.0)\n",
      "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.15.0)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.33.6)\n",
      "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
      "Collecting tensorboard<1.15.0,>=1.14.0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/91/2d/2ed263449a078cd9c8a9ba50ebd50123adf1f8cfbea1492f9084169b89d9/tensorboard-1.14.0-py3-none-any.whl (3.1MB)\n",
      "\u001b[K     |████████████████████████████████| 3.2MB 32.4MB/s \n",
      "\u001b[?25hRequirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.2.2)\n",
      "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (3.10.0)\n",
      "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.12.0)\n",
      "Collecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3c/d5/21860a5b11caf0678fbc8319341b0ae21a07156911132e0e71bffed0510d/tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488kB)\n",
      "\u001b[K     |████████████████████████████████| 491kB 37.4MB/s \n",
      "\u001b[?25hRequirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.0.8)\n",
      "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
      "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (0.1.8)\n",
      "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.17.4)\n",
      "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.11.2)\n",
      "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.1.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (0.16.0)\n",
      "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (41.4.0)\n",
      "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (2.8.0)\n",
      "Installing collected packages: tensorboard, tensorflow-estimator, tensorflow\n",
      "  Found existing installation: tensorboard 1.12.2\n",
      "    Uninstalling tensorboard-1.12.2:\n",
      "      Successfully uninstalled tensorboard-1.12.2\n",
      "  Found existing installation: tensorflow-estimator 1.13.0\n",
      "    Uninstalling tensorflow-estimator-1.13.0:\n",
      "      Successfully uninstalled tensorflow-estimator-1.13.0\n",
      "  Found existing installation: tensorflow 1.13.0rc1\n",
      "    Uninstalling tensorflow-1.13.0rc1:\n",
      "      Successfully uninstalled tensorflow-1.13.0rc1\n",
      "Successfully installed tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n"
     ]
    },
    {
     "data": {
      "application/vnd.colab-display-data+json": {
       "pip_warning": {
        "packages": [
         "tensorflow"
        ]
       }
      }
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "!pip install tensorflow==1.14.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "colab_type": "code",
    "id": "MstG9-zlqdsZ",
    "outputId": "1b80226a-41c5-40e5-e585-f62f0c645e8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.6 / client 1.5.4)\n",
      "Downloading sampleSubmission.csv to /content\n",
      "  0% 0.00/86.8k [00:00<?, ?B/s]\n",
      "100% 86.8k/86.8k [00:00<00:00, 82.5MB/s]\n",
      "Downloading test1.zip to /content\n",
      " 99% 268M/271M [00:03<00:00, 89.3MB/s]\n",
      "100% 271M/271M [00:04<00:00, 70.4MB/s]\n",
      "Downloading train.zip to /content\n",
      " 96% 521M/543M [00:10<00:00, 28.6MB/s]\n",
      "100% 543M/543M [00:10<00:00, 52.6MB/s]\n",
      "mkdir: cannot create directory ‘train’: File exists\n",
      "mv: cannot stat '*.jpg': No such file or directory\n",
      "mkdir: cannot create directory ‘test1’: File exists\n",
      "mv: cannot stat '*.jpg': No such file or directory\n",
      "CPU times: user 436 ms, sys: 96 ms, total: 532 ms\n",
      "Wall time: 42.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# 运行前把kaggle.json换掉\n",
    "!pip install -U -q kaggle\n",
    "!mkdir -p ~/.kaggle\n",
    "!echo '{\"username\":\"pupil1\",\"key\":\"64bb04e02cc162ec6508f77fe18afbcc\"}' > ~/.kaggle/kaggle.json\n",
    "!chmod 600 ~/.kaggle/kaggle.json\n",
    "!mkdir -p dogs-vs-cats\n",
    "!kaggle competitions download -c dogs-vs-cats\n",
    "!mv *.zip dogs-vs-cats/\n",
    "!cd /content/dogs-vs-cats/;unzip train.zip > train.log\n",
    "!cd /content/dogs-vs-cats;mkdir train;mv *.jpg train/\n",
    "!cd /content/dogs-vs-cats/;unzip test1.zip > test.log\n",
    "!cd /content/dogs-vs-cats;mkdir test1;mv *.jpg test1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g5GnJX6Ix9cJ"
   },
   "source": [
    "#分割数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 148
    },
    "colab_type": "code",
    "id": "Xo9SsYHKx8_2",
    "outputId": "05a53a09-054c-4878-f4b5-7be1b2950213"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total training cat images: 1000\n",
      "total training dog images: 1000\n",
      "total validation cat images: 500\n",
      "total validation dog images: 500\n",
      "total test cat images: 500\n",
      "total test dog images: 500\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "\n",
    "\n",
    "# # 5.2 - 使用小数据集来训练卷积神经网络\n",
    "\n",
    "# 猫狗图片识别任务\n",
    "# ２０００猫，２０００狗\n",
    "# 训练集：2000条\n",
    "# 验证集：1000条\n",
    "# 测试集:1000条\n",
    "\n",
    "# 这个部分，我们使用少量数据来处理这个难题：\n",
    "# 首先用２０００的数据集训练一个ｃｏｎｖｎｅｔ\n",
    "# 我们不使用任何正则化，以此来设置一个ｂａｓｅｌｉｎｅ\n",
    " \n",
    "# 注意，欠拟合我们看训练集的预测\n",
    "# 过拟合我们看对验证集的预测。 \n",
    "# 然后我们会引入数据增强技术来减缓过拟合问题。\n",
    "\n",
    "# 下面这句话不太好翻译，保留原文：\n",
    "# By leveraging data augmentation, we will improve \n",
    "# our network to reach an accuracy of 82%.\n",
    "\n",
    "\n",
    "# 训练一个小网络\n",
    "# 对预训练的网络进行特征提取，\n",
    "# 对预训练的网络进行微调\n",
    "# 将会称为你未来处理小型数据集的重要工具\n",
    "\n",
    "# 卷积神经网络学到的是局部的、平移不变的特性，他们对感知问题十分有效\n",
    "\n",
    "# 图片是JPEG格式，他们像这样：\n",
    "# https://s3.amazonaws.com/book.keras.io/img/ch5/cats_vs_dogs_samples.jpg\n",
    "\n",
    "# 对于图片分类任务，我们使用１０%的数据集也会达到使用１００％的数据集的类似的效果\n",
    "\n",
    "# 我们使用其中一部分数据集：\n",
    "# 猫训练集:1000\n",
    "# 猫验证集：500\n",
    "# 猫测试集：500\n",
    "\n",
    "# 狗训练集:1000\n",
    "# 狗验证集：500\n",
    "# 狗测试集：500\n",
    "\n",
    "\n",
    "#---------------------------代码清单5-4--------------------------------------\n",
    "import os, shutil\n",
    "\n",
    "#\n",
    "original_dataset_dir = '/content/dogs-vs-cats/train'#原始完整train\n",
    "\n",
    "#存放小数据集的路径\n",
    "base_dir = '/content/cats_and_dogs_small'\n",
    "os.mkdir(base_dir)\n",
    "\n",
    "# 下面是各种路径名字的拼接\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "os.mkdir(train_dir)#新建文件夹\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "os.mkdir(validation_dir)\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "os.mkdir(test_dir)\n",
    "\n",
    "# 存放猫的训练集\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "os.mkdir(train_cats_dir)\n",
    "\n",
    "# 存放狗的训练集\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "os.mkdir(train_dogs_dir)\n",
    "\n",
    "# 存放猫的验证集\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "os.mkdir(validation_cats_dir)\n",
    "\n",
    "# 存放狗的验证集\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "os.mkdir(validation_dogs_dir)\n",
    "\n",
    "#存放猫的测试集\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "os.mkdir(test_cats_dir)\n",
    "\n",
    "#存放狗的测试集\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "os.mkdir(test_dogs_dir)\n",
    "\n",
    "# 把前面１０００张猫图片拷贝到训练集目录下面\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "#把接下来的５００张猫的图片拷贝到验证集下面\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "#把接下来的５００张猫的图片拷贝到测试集下面\n",
    "fnames = ['cat.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_cats_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# 把前面１０００张狗图片拷贝到训练集目录下面\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(train_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# 把前面５００张狗图片拷贝到验证集目录下面\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(validation_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "    \n",
    "# Copy next 500 dog images to test_dogs_dir\n",
    "fnames = ['dog.{}.jpg'.format(i) for i in range(1500, 2000)]\n",
    "for fname in fnames:\n",
    "    src = os.path.join(original_dataset_dir, fname)\n",
    "    dst = os.path.join(test_dogs_dir, fname)\n",
    "    shutil.copyfile(src, dst)\n",
    "\n",
    "\n",
    "# As a sanity check, let's count how many pictures we have in each training split (train/validation/test):\n",
    "print('total training cat images:', len(os.listdir(train_cats_dir)))\n",
    "print('total training dog images:', len(os.listdir(train_dogs_dir)))\n",
    "print('total validation cat images:', len(os.listdir(validation_cats_dir)))\n",
    "print('total validation dog images:', len(os.listdir(validation_dogs_dir)))\n",
    "print('total test cat images:', len(os.listdir(test_cats_dir)))\n",
    "print('total test dog images:', len(os.listdir(test_dogs_dir)))\n",
    "\n",
    "\n",
    "# 训练集:2000\n",
    "# 验证集:1000\n",
    "# 测试集：1000\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q2Xhu98ux5gE"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-2rzVXpMCdtG"
   },
   "source": [
    "#下面是训练代码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 791
    },
    "colab_type": "code",
    "id": "umAUzxnVvtQt",
    "outputId": "558b67fa-240e-475d-f0a7-81114ec5ef76"
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnRLYyU5ec9kpgZ3s5DWX/w/6k1PeHn/v3wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW9hjbv8f1sJ6YKzROSAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "import os\n",
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras import models\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import load_model\n",
    "from keras.utils.vis_utils import plot_model\n",
    "import json\n",
    "from keras.preprocessing import image\n",
    "from keras import optimizers\n",
    "\n",
    "# 你已经知道一系列技术来减缓过拟合，例如dropout和Ｌ２正则，\n",
    "# 下面我们要介绍一种新技术：数据增强\n",
    "\n",
    "# 因为我们只有相对少的训练集(2000),需要考虑过拟合。\n",
    "\n",
    "\n",
    "base_dir = '/content/cats_and_dogs_small'\n",
    "train_dir = os.path.join(base_dir, 'train')\n",
    "validation_dir = os.path.join(base_dir, 'validation')\n",
    "test_dir = os.path.join(base_dir, 'test')\n",
    "\n",
    "# Directory with our training cat pictures\n",
    "train_cats_dir = os.path.join(train_dir, 'cats')\n",
    "train_dogs_dir = os.path.join(train_dir, 'dogs')\n",
    "validation_cats_dir = os.path.join(validation_dir, 'cats')\n",
    "validation_dogs_dir = os.path.join(validation_dir, 'dogs')\n",
    "test_cats_dir = os.path.join(test_dir, 'cats')\n",
    "test_dogs_dir = os.path.join(test_dir, 'dogs')\n",
    "\n",
    "\n",
    "datagen = ImageDataGenerator(\n",
    "      rotation_range=40,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      shear_range=0.2,\n",
    "      zoom_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    "\n",
    "\n",
    "#下面是一部分数据增强技术：\n",
    "# * `rotation_range` ：a value in degrees (0-180), a range within which to randomly rotate pictures.\n",
    "# * `width_shift` and `height_shift` are ranges (as a fraction of total width or height) within which to randomly translate pictures \n",
    "# vertically or horizontally.\n",
    "# * `shear_range` ：randomly applying shearing transformations.\n",
    "# * `zoom_range` ： randomly zooming inside pictures.\n",
    "# * `horizontal_flip` is for randomly flipping half of the images horizontally -- relevant when there are no assumptions of horizontal \n",
    "# asymmetry (e.g. real-world pictures).\n",
    "# * `fill_mode` is the strategy used for filling in newly created pixels, which can appear after a rotation or a width/height shift.\n",
    "# \n",
    "# 让我们看下增强后的图片：\n",
    "# This is module with image preprocessing utilities\n",
    "# ５－１２　显示几个随机增强后的训练图像\n",
    "\n",
    "fnames = [os.path.join(train_cats_dir, fname) for fname in os.listdir(train_cats_dir)]\n",
    "# We pick one image to \"augment\"\n",
    "img_path = fnames[3]\n",
    "\n",
    "# Read the image and resize it\n",
    "img = image.load_img(img_path, target_size=(150, 150))\n",
    "\n",
    "# Convert it to a Numpy array with shape (150, 150, 3)\n",
    "x = image.img_to_array(img)\n",
    "\n",
    "# Reshape it to (1, 150, 150, 3)\n",
    "x = x.reshape((1,) + x.shape)\n",
    "\n",
    "# The .flow() command below generates batches of randomly transformed images.\n",
    "# It will loop indefinitely, so we need to `break` the loop at some point!\n",
    "i = 0\n",
    "for batch in datagen.flow(x, batch_size=1):\n",
    "    imgplot = plt.imshow(image.array_to_img(batch[0]))\n",
    "    i += 1\n",
    "    name=\"data_augment_\"+str(i)+\".jpg\"\n",
    "    plt.savefig(name)\n",
    "    plt.figure(i)\n",
    "    if i % 4 == 0:\n",
    "        break\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# 我们接下来训练的神经网络使用了数据增强技术，虽然能减轻过拟合，但依然不足以完全摆脱过拟合\n",
    "\n",
    "\n",
    "def result_plot(history):\n",
    "    print(\"---------------进入绘图函数---------------------------\")\n",
    "    # Let's plot the loss and accuracy of the model over the training and validation data during training:\n",
    "    acc = history['acc']\n",
    "    val_acc = history['val_acc']\n",
    "    loss = history['loss']\n",
    "    val_loss = history['val_loss']\n",
    "    \n",
    "    epochs = range(len(acc))\n",
    "    \n",
    "    plt.plot(epochs, acc, 'bo', label='Training acc')\n",
    "    plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
    "    plt.title('Training and validation accuracy')\n",
    "    plt.legend()\n",
    "    plt.savefig('./5_2-2-Training-and-validation-accuracy.jpg')\n",
    "    # plt.figure()\n",
    "    plt.plot(epochs, loss, 'bo', label='Training loss')\n",
    "    plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
    "    plt.title('Training and validation loss')\n",
    "    plt.legend()\n",
    "    plt.savefig('./5_2-2-Training-and-validation-loss.jpg')\n",
    "    # plt.show()\n",
    "\n",
    "\n",
    "def create_model():\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='relu',input_shape=(150, 150, 3)))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "    model.add(tf.keras.layers.MaxPooling2D((2, 2)))\n",
    "    \n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dropout(0.5))\n",
    "    model.add(tf.keras.layers.Dense(512, activation='relu'))\n",
    "    model.add(tf.keras.layers.Dense(1, activation='sigmoid'))\n",
    "    return model\n",
    "\n",
    "\n",
    "def train(epochs,steps_per_epoch):\n",
    "\n",
    "    import os\n",
    "    resolver = tf.contrib.cluster_resolver.TPUClusterResolver('grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
    "    tf.contrib.distribute.initialize_tpu_system(resolver)\n",
    "    # strategy = tf.contrib.distribute.TPUStrategy(resolver)\n",
    "\n",
    "    # with strategy.scope():\n",
    "    model=create_model()\n",
    "    model.compile(\n",
    "    optimizer=tf.keras.optimizers.RMSprop(lr=1e-4),\n",
    "    loss='binary_crossentropy',metrics=['acc'])\n",
    "\n",
    "    \n",
    "    \n",
    "    # Let's train our network using data augmentation and dropout:\n",
    "    train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        rotation_range=40,\n",
    "        width_shift_range=0.2,\n",
    "        height_shift_range=0.2,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True,)\n",
    "    \n",
    "    # Note that the validation data should not be augmented!\n",
    "    test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "    \n",
    "    train_generator = train_datagen.flow_from_directory(\n",
    "            # This is the target directory\n",
    "            train_dir,\n",
    "            # All images will be resized to 150x150\n",
    "            target_size=(150, 150),\n",
    "            batch_size=32,\n",
    "            # Since we use binary_crossentropy loss, we need binary labels\n",
    "            class_mode='binary')\n",
    "    \n",
    "    validation_generator = test_datagen.flow_from_directory(\n",
    "            validation_dir,\n",
    "            target_size=(150, 150),\n",
    "            batch_size=32,\n",
    "            class_mode='binary')\n",
    "\n",
    "  \n",
    "\n",
    "    history = model.fit_generator(\n",
    "    train_generator,\n",
    "    steps_per_epoch=steps_per_epoch,\n",
    "    epochs=epochs,\n",
    "    validation_data=validation_generator,\n",
    "    validation_steps=50)\n",
    "    model.save('cats_and_dogs_small_2.h5')#保存模型\n",
    "    \n",
    "    return history,model\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 55
    },
    "colab_type": "code",
    "id": "wQWGx5JTNzVB",
    "outputId": "ad9a025d-19e8-435d-cfa3-7c5e6fa8b0c7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6 µs, sys: 1e+03 ns, total: 7 µs\n",
      "Wall time: 9.54 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def top(epochs,steps_per_epoch):\n",
    "    if os.path.exists('cats_and_dogs_small_2.h5')==True:#如果当前路径存在模型文件，那么就直接读取模型\n",
    "        # 保存网络结构，载入网络结构\n",
    "        network = load_model('cats_and_dogs_small_2.h5') \n",
    "        print(\"输出权重\",network.get_weights())\n",
    "        plot_model(network, to_file='5.2-2.png', show_shapes=True)\n",
    "        print(\"------------------------输出权重的维度-------------------\")\n",
    "        weight_list=network.get_weights()\n",
    "        for item in weight_list:\n",
    "            print(\"当前权重矩阵维度是\",np.shape(item))\n",
    "        print(\"-----------------------------------------------------\")\n",
    "        load_dict=''\n",
    "        with open(\"5_2-2-history.json\",'r') as load_f:\n",
    "            load_dict = json.load(load_f)\n",
    "        result_plot(load_dict)\n",
    "        return '',''#调试用\n",
    "    else:\n",
    "        history,model=train(epochs,steps_per_epoch)\n",
    "        print(type(history))\n",
    "        print(type(history.history))#dict\n",
    "        return history,model\n",
    "    #否则，就重新开始训练模型，并且保存模型文件\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "H7jMUEvVYLNK",
    "outputId": "10541221-a8eb-40ae-d78f-c8a55bfd90f3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6DeKyfXhXBp2"
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "history,model=top(epochs=30,steps_per_epoch=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 56
    },
    "colab_type": "code",
    "id": "7sQzohoLnoRr",
    "outputId": "de6d17ce-4cc9-4475-b215-4c42a46cbb7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'loss': [0.6924009898094216, 0.6808271318224807, 0.6647207402821743, 0.6519279937551479, 0.6438765609264374, 0.6324761289025321, 0.6192201354994846, 0.6122117436895467, 0.5996004166315548, 0.5914058739488776, 0.5766409737380904, 0.5695275113438115, 0.5544097811886759, 0.5620646967959764, 0.5516283144854536, 0.5434375236732791, 0.5435465906732645, 0.5438331272733871, 0.5252734817037679, 0.5331116968935187, 0.5278026371745009, 0.5246298587081408, 0.5181160566180644, 0.5007155029917482, 0.5022277910299976, 0.5091851690606256, 0.504535011570863, 0.5140453328439338, 0.48590706725313204, 0.4835158444229682], 'acc': [0.52051765, 0.5606156, 0.6054293, 0.62215906, 0.6278125, 0.6472081, 0.65829146, 0.6676136, 0.6768216, 0.68560606, 0.6928392, 0.70296717, 0.71622473, 0.7019472, 0.7140151, 0.71875, 0.72016335, 0.7214196, 0.73674244, 0.72948235, 0.73775125, 0.740846, 0.7462121, 0.7553392, 0.74747473, 0.7493719, 0.7537879, 0.73586684, 0.7613636, 0.77481157], 'val_loss': [0.6821201169490814, 0.6658313429355621, 0.6473634135723114, 0.6416783463954926, 0.621146309375763, 0.6077759689092637, 0.603833794593811, 0.6105316317081452, 0.5652504140138626, 0.5716297441720962, 0.6119960820674897, 0.578541601896286, 0.5163113361597061, 0.5986557596921921, 0.5129951196908951, 0.5162590724229813, 0.5835754758119583, 0.5123691642284394, 0.5046964484453201, 0.5023768663406372, 0.5042670500278473, 0.5021459287405015, 0.5080152261257171, 0.4980285727977753, 0.504283014535904, 0.513545041680336, 0.4849842154979706, 0.4821097260713577, 0.4994533234834671, 0.496483359336853], 'val_acc': [0.55837566, 0.5913706, 0.6256345, 0.62309647, 0.6389594, 0.65926397, 0.6751269, 0.65482235, 0.69860405, 0.69606596, 0.6643401, 0.6897208, 0.74175125, 0.6782995, 0.7430203, 0.7290609, 0.6681472, 0.74111676, 0.7354061, 0.75634515, 0.7506345, 0.74365485, 0.7404822, 0.74428934, 0.74175125, 0.7506345, 0.7620558, 0.7620558, 0.7544416, 0.74365485]}\n"
     ]
    }
   ],
   "source": [
    "print(history.history)#这个有点坑，因为ｊｓｏｎ不支持单引号，所以无法ｄｕｍｐ\n",
    "json_data=json.loads(str(history.history).replace(\"'\", '\"'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 56
    },
    "colab_type": "code",
    "id": "FipVNmMUzCO4",
    "outputId": "2500c5d7-5143-4a7d-9f17-8290ffadaba6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "json_data= {'loss': [0.6924009898094216, 0.6808271318224807, 0.6647207402821743, 0.6519279937551479, 0.6438765609264374, 0.6324761289025321, 0.6192201354994846, 0.6122117436895467, 0.5996004166315548, 0.5914058739488776, 0.5766409737380904, 0.5695275113438115, 0.5544097811886759, 0.5620646967959764, 0.5516283144854536, 0.5434375236732791, 0.5435465906732645, 0.5438331272733871, 0.5252734817037679, 0.5331116968935187, 0.5278026371745009, 0.5246298587081408, 0.5181160566180644, 0.5007155029917482, 0.5022277910299976, 0.5091851690606256, 0.504535011570863, 0.5140453328439338, 0.48590706725313204, 0.4835158444229682], 'acc': [0.52051765, 0.5606156, 0.6054293, 0.62215906, 0.6278125, 0.6472081, 0.65829146, 0.6676136, 0.6768216, 0.68560606, 0.6928392, 0.70296717, 0.71622473, 0.7019472, 0.7140151, 0.71875, 0.72016335, 0.7214196, 0.73674244, 0.72948235, 0.73775125, 0.740846, 0.7462121, 0.7553392, 0.74747473, 0.7493719, 0.7537879, 0.73586684, 0.7613636, 0.77481157], 'val_loss': [0.6821201169490814, 0.6658313429355621, 0.6473634135723114, 0.6416783463954926, 0.621146309375763, 0.6077759689092637, 0.603833794593811, 0.6105316317081452, 0.5652504140138626, 0.5716297441720962, 0.6119960820674897, 0.578541601896286, 0.5163113361597061, 0.5986557596921921, 0.5129951196908951, 0.5162590724229813, 0.5835754758119583, 0.5123691642284394, 0.5046964484453201, 0.5023768663406372, 0.5042670500278473, 0.5021459287405015, 0.5080152261257171, 0.4980285727977753, 0.504283014535904, 0.513545041680336, 0.4849842154979706, 0.4821097260713577, 0.4994533234834671, 0.496483359336853], 'val_acc': [0.55837566, 0.5913706, 0.6256345, 0.62309647, 0.6389594, 0.65926397, 0.6751269, 0.65482235, 0.69860405, 0.69606596, 0.6643401, 0.6897208, 0.74175125, 0.6782995, 0.7430203, 0.7290609, 0.6681472, 0.74111676, 0.7354061, 0.75634515, 0.7506345, 0.74365485, 0.7404822, 0.74428934, 0.74175125, 0.7506345, 0.7620558, 0.7620558, 0.7544416, 0.74365485]}\n"
     ]
    }
   ],
   "source": [
    "print(\"json_data=\",json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 36
    },
    "colab_type": "code",
    "id": "XUjwEuiOPIsy",
    "outputId": "7297ad21-8b1d-4971-883e-9d38e85a2e76"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行结束\n"
     ]
    }
   ],
   "source": [
    "#为了方便在notebook中调试，下面的语句还是单独放在外面吧\n",
    "with open('5_2-2-history.json', 'w') as f:#保存训练过程中的记录，便于后面画画。\n",
    "    json.dump(json_data,f)\n",
    "print(\"运行结束\")#一定要有这一句，这样在log里面就能看到是否真的结束了。"
   ]
  }
 ],
 "metadata": {
  "accelerator": "TPU",
  "colab": {
   "collapsed_sections": [],
   "name": "“欢迎使用 Colaboratory”的副本",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
